Wednesday
1:50 p.m.–2:20 p.m.

Python for Public Health: Building Statistical Models of Ciliary Motion

Description

Cilia, microscopic hairs lining nearly every cell surface in your body, play a major role in developmental and sinopulmonary health. As such, deriving quantitative properties of their motion is compelling for both clinical and research purposes. Here we demonstrate a computational pipeline built entirely in Python for analyzing ciliary motion using a variety of machine learning techniques.

Abstract

Motile cilia lining the nasal and bronchial passages beat synchronously to clear mucus and foreign matter from the respiratory tract. This mucociliary defense mechanism is essential for pulmonary health, because respiratory ciliary motion defects, such as those in patients with primary ciliary dyskinesia (PCD) or congenital heart disease, can cause severe sinopulmonary conditions necessitating organ transplant.
The visual examination of nasal or bronchial biopsies is critical for the diagnosis of ciliary motion defects, but these analyses are highly subjective and error-prone. Although ciliary beat frequency can be computed, this metric cannot sensitively characterize ciliary motion defects. Furthermore, ciliopathies can present without any structural defects, limiting the use of other detection methods, such as electron microscopy. Therefore, an unbiased, computational method for analyzing ciliary motion is clinically compelling.
We present a computational pipeline built in Python using techniques from computer vision and machine learning to decompose ciliary motion into quantitative properties. Using this framework, we constructed feature vectors to perform classification of the ciliary motion as normal or abnormal. We achieved over 90% classification accuracy in two independent data cohorts composed of patients with congenital heart disease, PCD, or heterotaxy, as well as healthy controls.